为文本生成矢量图设计了基于视觉的评测框架，更贴近人眼感知。
SVGEval: A Vision-Grounded Framework for Perceptual-Quality Benchmarking and Evaluation in Text-to-SVG Generation
- 通过渲染图像评估模型输出，而非仅看SVG代码。
- 发现模型在几何和布局判断上明显落后于语义与美学评价。
- 提供可解释的评分器，适合研究与优化文本到SVG生成模型。
多模态大模型越来越多地用于生成可缩放矢量图形（SVG），但可靠的评估方法仍不充分。现有评测通常以代码为中心，或在将SVG渲染为位图后借用图像指标，无法反映人类感知，也忽略了几何和空间构图等SVG特有质量。我们提出SVGEval，一个基于视觉的多模态基准，用于人类对齐的SVG质量评估。该框架显式引入视觉渲染结果，评估模型是否能判断渲染后的实际效果，而非仅分析SVG代码，并通过多轮人工标注与专家精修获得高质量标注。对代表性多模态模型的系统评估显示：模型在语义对齐和美学方面表现较好，但在几何和布局判断上存在明显短板。基于SVGEval，我们训练了一个可解释的SVG质量评分器，输出多维度分数并附带文本推理。消融实验表明，显式的视觉锚定和推理监督对空间与几何评估尤为关键。SVGEval为多模态时代下的SVG生成评估与改进提供了可靠测试平台与实用评分工具。
原文摘要 · Abstract (English)
Multimodal large models are increasingly used to generate scalable vector graphics (SVG), but reliable evaluation remains underexplored. Existing protocols are often code-centric or borrow raster-image metrics after rendering SVGs, which fail to reflect human perception and overlook SVG-specific qualities such as geometry and spatial composition. We introduce SVGEval, a vision-grounded multimodal benchmark for human-aligned SVG quality assessment. SVGEval explicitly incorporates visual renderings to evaluate whether models can judge the rendered outcome rather than only inspect SVG code, and provides high-quality annotations obtained via multi-round human labeling with expert refinement. Systematic evaluations across representative multimodal models reveal a clear gap: models perform relatively well on semantic alignment and aesthetics, yet struggle on geometry- and layout-related judgments. Building on SVGEval, we train an explainable SVG quality scorer that outputs multi-aspect scores with textual rationales. Ablations show that explicit visual grounding and reasoning supervision are crucial, especially for spatial and geometric assessment. SVGEval offers a reliable testbed and practical scorer for evaluating and improving SVG generation in the era of multimodal models.
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